The early detection of arrhythmia is very important for the cardiac patients. This done by analyzing the electrocardiogram (ECG) signals and extracting some features from them. These features can be used in the classification of different types of arrhythmias. In this paper, we present three different algorithms of features extraction: Fourier transform (FFT), Autoregressive modeling (AR), and Principal Component Analysis (PCA). The used classifier will be Artificial Neural Networks (ANN). We observed that the system that depends on the PCA features give the highest accuracy. The proposed techniques deal with the whole 3 second intervals of the training and testing data. We reached the accuracy of 92.7083 compared to 84.4 for the reference that work on a similar data.

ECG\abc.mat
...\cardiac.m
...\gui_export.mat
...\imagesample.JPG
...\Sample Data\events\ti3108.mat
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...\...........\......\ti3119.mat
...\...........\IBIs\i3108_im.mat
...\...........\....\i3111_im.mat
...\...........\....\i3114_im.mat
...\...........\....\i3118_im.mat
...\...........\....\i3119_im.mat
...\...........\events
...\...........\IBIs
...\Sample Data
ECG

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